Precision Agriculture: Machine Learning based Weather Prediction – A Comparative Study of Adaboost and Modified Adaboost Algorithm

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : A. Lavanya, B. Swapna

10.14445/23488549/IJECE-V12I7P136

How to Cite?

A. Lavanya, B. Swapna, "Precision Agriculture: Machine Learning based Weather Prediction – A Comparative Study of Adaboost and Modified Adaboost Algorithm," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 7, pp. 459-471, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I7P136

Abstract:

Optimal crop growth through irrigation is achieved through the intelligent use of ensemble algorithms in predictive modelling, which depend on the massive amounts of information gathered and transmitted by various electronic devices and sensors pertaining to the crop's environment, well-being, and soil quality. Existing system: In traditional farming practices, the monitoring of environmental factors and soil conditions is often manual, resulting in delayed responses to potential issues, such as nutrient imbalances, water stress, or suboptimal climatic conditions. The delayed response in decision-making can result in decreased crop yield, inefficient resource use, and increased production costs. Many farmers rely on intuition or historical data, which do not account for the dynamic changes in the farm environment. As a result, there is an urgent need for systems that can offer real-time, data-driven insights into key environmental factors that influence crop health. Proposed system: This study presents an innovative framework of ML aimed at optimizing environmental management through continuous surveillance of essential weather parameters like temperature, air pressure, wind speed, and humidity to predict the future temperature with the Internet of Things. The objective of this article is to present the prediction of weather parameters through Adaboost and Modified Adaboost models, and to compare their performance indicators. Weather prediction with a modified Adaboost technique achieves an accuracy of 94%. By integrating these technologies, farmers can improve their farms' output and sustainability. They will also receive helpful information and be able to make informed decisions about irrigation and fertilization.

Keywords:

Machine Learning (ML), Weather prediction, Precision agriculture, Crop health, Adaboost and Modified Adaboost models.

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